Associate Director, Commercial AI Product Owner

About the role

The Associate Director, Commercial AI Product Owner is responsible for leading the transformation of advanced analytics into durable, scalable, and AI-powered solutions that directly accelerate commercial decision-making across the enterprise.

Responsibilities

• Tools, Automation, & AI Products Product Ownership
• Serve as dedicated product owner for the Agentic MMx (Marketing Mix) Platform - own its strategy, vision, roadmap, and stakeholder alignment in partnership with BI&T and Data Science.
• Drive the ongoing evolution of this self-service analytics hub to deliver advanced capabilities, including KDA, scenario simulation, investment planning, constrained optimization, and real-time decision support.
• Pod Leadership
• Lead the Tools & Automated Solutions cross functional team across BI&T, Data Science, TA Analytics, and analytics/engineering to architect, launch, and maintain scalable analytics solutions—covering dashboards, always-on insights, scenario simulators, measurement pipelines, and democratized KDA platforms.
• Collaboration
• Partner with BI&T, Data Science, TA Analytics, and engineering to deliver analytics-ready datasets, feature stores, semantic layers, and automated data pipelines, standardizing and accelerating insight generation.
• Efficiency and Innovation
• Champion automation, templated workflows, and platformization to reduce manual effort and external vendor reliance, maximizing reuse and operational efficiency.
• Identify opportunities for tool innovation and scalable enablement of best practice measurement solutions.
• Integrate decision science outputs into annual planning, CRM (e.g., Veeva), and omnichannel orchestration workflows via APIs and embedded dashboards to ensure sustained adoption and business impact.
• Develop monitoring and governance dashboards for real-time oversight of model/data health, user adoption, SLAs, drift/stability, and auditability.
• Agentic & AI Capability Development
• Architect and deploy autonomous and semi-autonomous analytics agents using multi-agent frameworks, enabling progression from descriptive analytics to causal analysis, root-cause insights, and predictive recommendations.
• Own the AI product lifecycle, manage proof-of-concept, pilot, rollout, and continuous optimization; establish robust governance covering safety, security, ethical standards, and privacy compliance.
• Cross-Functional Team Management & Enablement
• Lead cross-functional pods (BI&T engineers, analytics/data engineers, platform SMEs), managing product roadmaps, agile backlogs, and release cycles.
• Codify and promote engineering standards for deployment, MLOps, CI/CD, QA/testing, SLAs/SLOs, and RACI matrices for reliability and quality.
• Standardize and scale processes, maximize component reuse, minimize vendor dependence, and ensure governance and privacy compliance.
• Coach and enable enterprise and TA-aligned analytics teams, driving best practice adoption, technical enablement, and a culture of consistent delivery and innovation.
• Promote agile ways of working using collaboration platforms (e.g., Jira) for rapid development and communication.
• Advance Governance, Compliance & Trust
• Partner with Data Governance, Legal, and Privacy teams to define and enforce data/AI governance SLAs, RACI, privacy-by-design, and responsible/ethical AI controls.
• Ensure AI tools and solutions are explainable, auditable, and compliant; continuously monitor for bias, document decisions, and incorporate human-in-the-loop mechanisms where required.
• Lead structured change management and feedback loops to drive sustained tool adoption and measurable business outcomes.

Qualifications

• Advanced degree (MS/PhD preferred) in Data Science, Statistics, Computer Science, Econometrics, or related quantitative field.
• Minimum 5 years of hands-on experience in pharma commercial analytics or decision science.
• Proficiency with causal inference and incrementality tools (geo-experiments, matched markets, synthetic controls, uplift modeling).
• Expertise in Bayesian/hierarchical MMx, adstock/distributed lag, saturation/response curve modeling, and operationalizing these methodologies in automated pipelines and platforms.
• Proven experience launching and scaling agentic AI solutions (multi-agent systems, LLMs, RAG, semantic layers, real-time architectures).
• Deep proficiency in enabling agent collaboration, negotiation, and task orchestration, including coordination of agent roles/functions within a commercial analytics or decision science context.
• Proven experience operationalizing LLMs for commercial use cases—knowledge retrieval, summarization, generative analytics, automation of insight generation.
• Experience embedding AI analytics platforms into commercial workflows with enterprise-wide adoption.
• Extensive knowledge of pharmaceutical data (claims, APLD, specialty pharmacy, digital signals, promotional data).
• Strong foundation in AI governance (risk management, security, privacy, model monitoring, human-in-the-loop) in regulated environments.
• Experience with cloud analytics platforms (Databricks, Snowflake, Spark), MLOps, and BI tools; understanding of HIPAA, GDPR/CCPA and regulatory standards.
• Mastery of programming and data science tools (Python, R), machine learning frameworks (scikit-learn, PyTorch), large-scale analytics environments, visualization platforms, and workflow automation.
• Familiarity with CRM (e.g., Veeva), omnichannel metrics, multi-touch attribution (MTA), and AI-driven next-best-action frameworks.
• Outstanding stakeholder engagement and communication skills—capable of translating complex analytics concepts into actionable business strategies.